AI Infrastructure Engineer — Scale ML Training & Inference

Triwill Group; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

OpenAI is seeking an AI Systems Engineer to scale infrastructure behind training and evaluation workflows. You will own projects from bottleneck identification to deployment, combining distributed systems engineering with performance tuning and collaboration with researchers.

You will build shared inference and grading platforms, improve scheduling, and develop observability tools to help researchers run experiments efficiently and reliably, with a strong culture of safety and human-centric

Qualifications

• Strong foundations in software engineering and distributed systems.
• Experience building or operating large-scale ML infrastructure.
• Familiarity with GPU performance optimization and observability tooling.
• Ability to own projects end-to-end from design to deployment.

Responsibilities

• Build and operate infrastructure for large-scale training and evaluation, improving reliability, throughput, and resource efficiency.
• Develop shared inference and grading platforms with automated capacity management, health monitoring, and visibility into performance.
• Improve compute scheduling and resource allocation to reduce idle GPU time and speed up workload recovery from failures.
• Diagnose bottlenecks across training, inference, and orchestration, coordinating with teams to boost end-to-end performance.
• Build self-service tools and automated validation to empower researchers to launch experiments with less manual intervention.

Skills

Distributed systems

Performance optimization

ML infrastructure

Inference systems

GPU performance

Infrastructure tooling

Capacity management

Health monitoring

Compute scheduling

Resource allocation

Debugging

Observability

Job description

OpenAI is seeking an AI Systems Engineer to scale infrastructure behind training and evaluation workflows. You will own projects from bottleneck identification to deployment, combining distributed systems engineering with performance tuning and collaboration with researchers.

You will build shared inference and grading platforms, improve scheduling, and develop observability tools to help researchers run experiments efficiently and reliably, with a strong culture of safety and human-centric